# CT Field of View Extension Using Combined Channels Extension and Deep Learning Methods

arXiv: https://arxiv.org/abs/1908.09529 · HTML: https://mlsanity.com/paper/1908.09529/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective](https://mlsanity.com/paper/2207.06551/) (2022)
- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)
- [Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images](https://mlsanity.com/paper/2406.04769/) (2024)
- [Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction](https://mlsanity.com/paper/2606.06039/) (2026)
- [Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography](https://mlsanity.com/paper/2606.31318/) (2026)

## Abstract (arXiv)

This paper proposes a method to extend the field of view of computed tomography images. In a first step, the field of view is increased by extrapolating linearly the outer channels in the sinogram space. The modified sinogram is then used to reconstruct extended field of view (EFoV) images containing artifacts due to the channels extension. In a second step, those artifacts are reduced by a deep learning network in image space. The proposed method has been evaluated on a collection of clinical scans. The resulting volumes have been checked for consistency and plausibility and compared to an existing state of the art EFoV method.